The present disclosure relates generally to data privacy for medical images and in particular to systems and methods for obscuring surface anatomical features in medical images.
Medical imaging technology, such as magnetic resonance imaging (MM), computerized axial tomography (CT or CAT) scans, and the like, provides detailed three-dimensional (3D) views of a patient's internal anatomical structures (e.g., tissues and/or organs). The images may be captured as a set of two-dimensional (2D) “slices” through the patient's body, from which a 3D representation of the imaged portion of the patient's body can be generated. The 3D representation, referred to herein as a “medical image,” consists of an arrangement of three-dimensional image elements (referred to as “voxels”) with assigned intensity values based on the imaging process.
In addition to diagnosing a condition in an individual patient, medical images may also be useful in medical research and/or training. For instance, medical knowledge can be advanced by analyzing medical images of a number of patients with a known condition in order to identify features that may be relevant for diagnosis of future patients. For such reasons, it may be desirable to share medical images with persons other than the patient and the patient's healthcare provider(s).
However, sharing of medical images with third parties may unacceptably compromise patient privacy. For instance, a medical image may provide sufficiently detailed information about surface anatomical features of the patient (e.g., facial features such as shape of eyes, nose, mouth, ears, etc.) to allow the patient's identity to be determined (e.g., using the facial recognition ability of a person or automated system). Consequently, sharing such images could be a violation of privacy protection laws or regulations. To enable sharing of medical images without compromising patient privacy, it would be desirable to modify a medical image in a way that obscures surface anatomical features (so that the patient cannot be recognized) without altering the medically-useful information (e.g., portions of the medical image representing internal anatomical structures). Such modifications are referred to as “de-identification” of an image.
Several de-identification techniques are currently in use. One such technique, used in the context of brain MRI, is referred to as “skull stripping.” This technique entails using a computer algorithm to identify and remove voxels that correspond to non-brain tissue from a medical image of a patient's brain, based on assumptions or models about the likely location of brain tissue in a medical image. In practice, skull stripping can be vulnerable to imaging artifacts, and voxels corresponding to brain tissue may be inadvertently removed. Manual intervention is generally required to prevent or correct such errors. In addition, non-brain tissue may be useful for some studies, and removing non-brain areas from the medical image can limit the usefulness of the image for research.
Another conventional de-identification technique is referred to as “defacing.” A facial probability map is created, defining the likelihood that voxels in a particular region would correspond to a patient's face. A rigid-body image registration algorithm is used to align a medical image to the facial probability map, allowing removal of voxels with a nonzero probability of corresponding to the patient's face. The defaced image hides the patient's facial features while preserving internal brain voxels. This technique requires a reliable facial probability map, and generating such maps has proven difficult. It is generally necessary to create the map manually or rely on an average across a number of images. Facial maps are also generally non-transferable across imaging modalities or datasets with high morphological variability. Further, defacing algorithms typically result in removal of some internal structures (such as nasal cavities), which may limit the usefulness of the image for research.
In general, existing techniques for de-identifying medical images are computationally intensive and/or require significant manual intervention. In addition, these techniques may be susceptible to error, as they rely on image registration techniques that may not be applicable in a particular case. Improved de-identification techniques for medical images would therefore be desirable.